Learning integrated online indexing for image databases

Bir Bhanu, Shan Qing, Jing Peng

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

Most of the current image retrieval systems use 'one-shot' queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper we present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behavior of the method for image retrieval.

Original languageEnglish
Pages789-793
Number of pages5
StatePublished - 1 Dec 1998
EventProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA
Duration: 4 Oct 19987 Oct 1998

Other

OtherProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3)
CityChicago, IL, USA
Period4/10/987/10/98

Fingerprint

Image retrieval
Feedback

Cite this

Bhanu, B., Qing, S., & Peng, J. (1998). Learning integrated online indexing for image databases. 789-793. Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, .
Bhanu, Bir ; Qing, Shan ; Peng, Jing. / Learning integrated online indexing for image databases. Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, .5 p.
@conference{70f3ca74c009470583e299fa40653ca3,
title = "Learning integrated online indexing for image databases",
abstract = "Most of the current image retrieval systems use 'one-shot' queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper we present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behavior of the method for image retrieval.",
author = "Bir Bhanu and Shan Qing and Jing Peng",
year = "1998",
month = "12",
day = "1",
language = "English",
pages = "789--793",
note = "null ; Conference date: 04-10-1998 Through 07-10-1998",

}

Bhanu, B, Qing, S & Peng, J 1998, 'Learning integrated online indexing for image databases', Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, 4/10/98 - 7/10/98 pp. 789-793.

Learning integrated online indexing for image databases. / Bhanu, Bir; Qing, Shan; Peng, Jing.

1998. 789-793 Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - Learning integrated online indexing for image databases

AU - Bhanu, Bir

AU - Qing, Shan

AU - Peng, Jing

PY - 1998/12/1

Y1 - 1998/12/1

N2 - Most of the current image retrieval systems use 'one-shot' queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper we present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behavior of the method for image retrieval.

AB - Most of the current image retrieval systems use 'one-shot' queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithms is used where weights measuring feature importance along input dimensions remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper we present a novel method that enables image retrieval procedures to continuously learn feature relevance based on user's feedback, and which is highly adaptive to query locations. Experimental results are presented that provide the objective evaluation of learning behavior of the method for image retrieval.

UR - http://www.scopus.com/inward/record.url?scp=0032296695&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0032296695

SP - 789

EP - 793

ER -

Bhanu B, Qing S, Peng J. Learning integrated online indexing for image databases. 1998. Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, .